Companies may record extremely large amounts of data by logging their customers' activities, e.g., purchasing transactions, web site visits, online search histories, customer care interactions, services usage, etc. Ideally, companies would like to use this data to find common patterns of activities, or even better, use this data to find reasons behind those activities. Based on this analysis, companies may be able to better understand their customers and, as a result, improve customer satisfaction and other aspects of their business. However, no tool exists that can process large amounts of data to identify patterns of activities common to different database sources and efficiently provide data analytics based on these common patterns of activities.